While the genomes of two people are >99% identical, the genetic differences encode amazing diversity in human traits and disease susceptibility. Understanding of the genetic architecture underlying human diversity in complex traits has been transformed by the application of genome-wide association studies (GWAS). However, GWAS are just the first step in understanding how genetic variants contribute to disease risk by impacting genes that encode components of cellular physiology. Elucidating this chain of causality from SNP to gene to cell biology to disease can serve to not only functionally validate the genetic association with disease but also reveal potential therapeutic targets. Therefore, there is a need for approaches that can facilitate identification of the cellular pathways regulated by human genetic variants associated with disease. The objectives of this application are to systematically reveal the shared genetic architecture connecting cellular physiology and disease susceptibility and to develop a database to facilitate hypothesis generation from these data. The experimental and computational tools are in place to carry this out successfully. GWAS of molecular and cellular traits have been generated by our lab and acquired from publicly available datasets. An extensively validated cellular GWAS platform called Hi-HOST (High-throughput human in vitro susceptibility testing) uses pathogens to stimulate fundamental cellular pathways. Hi-HOST combines precise measurement of cellular phenotypes in lymphoblastoid cell lines (LCLs) from nearly a thousand people with genome-wide association using 15 million genetic variants to identify genetic differences that underlie the phenotypic variation. With this platform, cellular responses of infectivity and replication, cytokine levels, and host cell death using 9 different pathogens have already been carried out and analyzed with family-based GWAS analysis on 148 cellular traits. Dozens of SNPs pass genome-wide significance have been incorporated into established workflows in the lab to validate their importance and determine mechanism. Additional datasets to expand our analyses beyond LCLs include metabolomics GWAS data from blood and urine and GWAS of immune cell levels. These molecular and cellular GWAS dataset will be integrated with human disease GWAS from the NHGRI/EMBL catalog. The central hypothesis is that the SNPs associated with each cellular phenotype and disease serve as a ?GWAS signature? that can be used to connect these different traits based on similarity and that the contribution of individual cellular traits to heritability of diseases can be estimated from comparing these signatures. Published methods developed by our own lab (CPAG (Cross-Phenotype Analysis of GWAS)), as well as by other investigators (LD-score regression) will be used to carry out these analyses. Thus, this project will integrate cellular and disease GWAS to create a re-interpretation of the human genome through the lens of cell biology. The project leverages existing datasets into a hypothesis generating engine for researchers looking to explore new diagnostic and therapeutic possibilities.